Agent-based modelling in archaeology

Angourakis - CDAL series

Cambridge, 5 February 2019

https://andros-spica.github.io/CDAL-Angourakis-2019/

Agent-based modelling in archaeology

Why do it, how to do it, and what to expect

Andreas Angourakis @AndrosSpica

available at https://andros-spica.github.io/CDAL-Angourakis-2019/
https://andros-spica.github.io/CDAL-Angourakis-2019/index.html?print-pdf (printable version)

click on images to consult source

McDonald Institute for Archaeological Research

The 'why '

Archaeology

Challenges

fragmentary, coarse data
indirect evidence
(mostly) not experimental
semantic ambiguity
few consensual models
debate on core aspects

Opportunities

long-term perspective
materiality
holistic, "naive", out-of-box thinking
permeability to other disciplines

The prism of complexity science

Complex system
complexity: number and diversity of causal relationships
nonlinearity, self-organisation and self-similarity
Unifying frameworks:
- theoretical (e.g., generative social science, socio-ecological systems)
- methodological (e.g., open databases, GIS, ABM)

Breeder pattern in Conway's Game of Life
derivative work: George (talk)Conways_game_of_life_breeder.png: Hyperdeath [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

Agent-based modelling (ABM )

dynamics
Formalisation
rules
population
bottom-up
stochasticity

→ simulation

→ defined logic rules

→ algorithms

→ distributed processes

→ emergent properties

→ probabilistic results

Flocking behaviour in 'Behavioral systems' by Danil Nagy in 'Generative Design', medium.com

Background: pseudo-code for the Gale–Shapley algorithm
to solve the Stable Marriage Problem

The 'how '

ABM modelling steps

Examples

Schelling's Segregation model

Definition:
the formation of racially mixed versus segregated neighborhoods in the United States (c. 1960-1970)
Design:
- two types of agents
- agents occupy a position in a grid
- adjacent positions are considered as neighborhood
- agent satisfaction = ratio of neighbors of the same type (homophily)
- unsatisfied agents move to another free position

Schelling, Thomas. 1971. “Dynamic Models of Segregation.” Journal of Mathematical Sociology 1 (143-186).

Examples

Schelling's Segregation model

Examples

Schelling's Segregation model

Examples

Artificial Anasazi

Axtell, R. L., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., … Parker, M. (2002). Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences, 99(Supplement 3), 7275–7279. https://doi.org/10.1073/pnas.092080799

Janssen, M. A. (2009). Understanding Artificial Anasazi. Journal of Artificial Societies and Social Simulation, 12(4). http://jasss.soc.surrey.ac.uk/12/4/13.html

Examples

HOMINIDS

Griffith, C. S., Long, B. L., and Sept, J. M. (2010). HOMINIDS: An agent-based spatial simulation model to evaluate behavioral patterns of early Pleistocene hominids. Ecological Modelling, 221(5), 738–760. https://doi.org/10.1016/j.ecolmodel.2009.11.009

Examples

MedLanD

Barton, C. M., Ullah, I. I. T., Bergin, S. M., Mitasova, H., and Sarjoughian, H. (2012). Looking for the future in the past: Long-term change in socioecological systems. Ecological Modelling, 241, 42–53. https://doi.org/10.1016/J.ECOLMODEL.2012.02.010

Examples

HouseholdsWorld

Rogers, J. D., Nichols, T., Emmerich, T., Latek, M., and Cioffi-Revilla, C. (2012). Modeling scale and variability in human–environmental interactions in Inner Asia. Ecological Modelling, 241, 5–14. https://doi.org/10.1016/J.ECOLMODEL.2011.11.025

Expectations

Grand challenges (method)

Disambiguation
Consolidation of shared models
Virtual laboratory
Interdisciplinary approach

Epstein, J. M. (2008). Why Model? Journal of Artificial Societies and Social Simulation, 11(4), 12. http://jasss.soc.surrey.ac.uk/11/4/12.html

Grand challenges (theory)

Emergence of social and socio-ecological systems
Resilience, adaptation, change, collapse
Space and mobility
Behaviour and cognition

Kintigh, K. W., Altschul, J. H., Beaudry, M. C., Drennan, R. D., Kinzig, A. P., Kohler, T. A., … Zeder, M. A. (2014). Grand challenges for archaeology. Proceedings of the National Academy of Sciences of the United States of America, 111(3), 879–880. https://doi.org/10.1073/pnas.1324000111

Social processes linked to materiality
Any spatial or time scale
Data standardization and collection
Theory building/hypothesis generation

Madella, M., Rondelli, B., Lancelotti, C., Balbo, A. L., Zurro, D., Rubio Campillo, X., & Stride, S. (2014). Introduction to Simulating the Past. Journal of Archaeological Method and Theory, 21(2), 251–257. https://doi.org/10.1007/s10816-014-9209-8

Rogers, J. D., & Cegielski, W. H. (2017). Opinion: Building a better past with the help of agent-based modeling. Proceedings of the National Academy of Sciences of the United States of America, 114(49), 12841–12844. https://doi.org/10.1073/pnas.1718277114

Cegielski, W. H., & Rogers, J. D. (2016). Rethinking the role of Agent-Based Modeling in archaeology. Journal of Anthropological Archaeology, 41, 283–298. https://doi.org/10.1016/J.JAA.2016.01.009

Caveats

NOT magical solution!
Still long way to set a "standard"
Steep learning curve (for all involved)
Inherits any theoretical/methodological biases
Validation, documentation and understanding often underdeveloped tasks

Future?

Project Highrise